Early detection and characterization of cardiac arrhythmias, such as myocardial ischemia and infarction, are critical for management of cardiac disorders and irregularities. Because cardiac arrhythmias can usually be indicated and monitored by cardiac electrophysiological changes, cardiac signals, both surface ECG signals and intra-cardiac electrograms, are monitored for diagnosis. Signal features corresponding to certain aspects of each heartbeat are extracted and analyzed to assist in the identification of various cardiac pathologies.
The surface electrocardiogram (ECG or, sometimes, EKG), obtained by recording the potential difference between two electrodes placed on the surface of the skin, produces a time-varying electrical signal indicative of changes in electrical potential during a heartbeat. A single normal cycle of the ECG represents the successive atrial and ventricular depolarization and repolarization that occur with each heartbeat as the cardiac muscle fibers alternately contract and relax. The electrical changes result in peaks and troughs of the ECG waveform, conventionally labeled P, Q, R, S and T, and recorded as deviations from the base or isoelectric level of the signal. Atrial depolarization produces the P-wave. Because the ventricles are larger in mass than the atria, the subsequent atrial repolarization is largely obscured by the QRS complex that represents the concurrent ventricular depolarization. The ST segment, culminating in the T-wave, represents ventricular repolarization.
Currently, waveform morphologies and corresponding medical parameter analysis of cardiac depolarization and repolarization processes for various portions of the ECG signal, including waveform shape and electrophysiological action potential changes of the P-wave, QRS complex, ST segment, and T-wave, are widely utilized for cardiac arrhythmia monitoring and identification. However, analysis of electrophysiological signals is often a subjective and time-intensive procedure, requiring expertise and clinical experience for proper interpretation.
In addition, there are several problems with current biomedical signal processing strategies for detection and monitoring of cardiac arrhythmias. Accurate analysis of electrophysiological signals is complicated by electrical noise, patient movement, and other bio-noise (respiration, etc), which can significantly affect and distort the small voltage cardiac electrophysiological signals, possibly resulting in unreliable diagnosis and improper characterization of various arrhythmias. Potential problems can occur, for example, in the detection and assessment of acute myocardial ischemia during a Percutaneous Transluminal Coronary Angioplasty (PTCA) procedure, often resulting in ST segment distortion. Ischemia occurs when the oxygen supplied by the coronary arteries is insufficient to meet the demands of the myocardium. This condition often appears during the PTCA procedure, as the artery is temporarily occluded. Similar diagnostic problems can occur in the detection of atrial fibrillation during an electrophysiologic (EP) procedure in the operating room, commonly resulting in P-wave distortion; and in long term signal diagnosis during Holster cardiac monitoring, which calls for analysis of cardiac signal morphology distortion; as well as in other arrhythmias.
Typically, the standards and criteria clinicians utilize for diagnosing cardiac pathology and malfunction focus on the main electrophysiological changes of the signal. However, because the pathology-related changes are distributed throughout both the depolarization and repolarization processes, current diagnosis and pathology evaluation methods, such as ST segment deviation as a criterion for monitoring ischemia cases, do not utilize all the information available in a signal. Ischemia is commonly diagnosed using ST segment analysis. The ST segment, lying between the end of the QRS complex and the initial deflection of the T-wave, is normally isoelectric. It corresponds to the first phase of ventricular repolarization following the QRS complex. Elevation or depression of the ST segment is considered clinically important as an indication of ischemia. In typical instances of ischemia, the ECG signal may not return to its baseline or isoelectric level (represented in the interval between the P-wave and the QRS complex) until after the T-wave. The level of the ST segment relative to the isoelectric level, termed the ST deviation, is conventionally measured at a single point 80 milliseconds after the end of the QRS complex. An ST deviation of 100 microvolts (μV) in surface ECG signals is a common standard for clinical significance and diagnosis of ischemia. However, ST segment changes, as well as QRS complex changes, are not necessarily obvious and are not easily qualitatively and quantitatively captured and characterized by such traditional methods as action potential displacements.
ST segment analysis presents significant deviation and error rates in ischemia/infarction characterization. Pathological changes, particularly during the ventricular depolarization within the QRS complex, may not be extracted and accurately captured by simple ST deviation measurement. Also, bio-noise may distort the ST segment, which is a low-frequency, shifting signal. There are currently no accepted criteria or methods for assessing the severity of ischemia via interpretation of the action potential voltage. Moreover, current clinical methods and strategies do not provide adaptive calculations and monitoring. The lack of adaptive monitoring may delay diagnosis, resulting in late medical decisions and delayed cardiac rhythm management. Furthermore, there are no standards or criteria for ST segment analysis to detect and characterize ischemia utilizing intra-cardiac electrograms. There is a need for a more sensitive, versatile and reliable signal analysis and characterization to capture minute pathological changes in cardiac action potentials.
Some recent research has begun to apply more sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis (e.g., FFT analysis, dominant frequency analysis, etc.), signal modeling analysis, and nonlinear entropy evaluation, but most research strategies focus on generating a new pathology index for qualitative characterization of cardiac arrhythmia.
Therefore, a need exists to apply different signal analysis algorithms to develop a more accurate and predictive measure of cardiac electrophysiological signals. A system and method according to invention principles addresses these deficiencies and related problems.